Intelligence isn’t about parameter count. It’s about time.

2026-02-25 · Source: Amazon Science homepage · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Data Science & Analytics · Depth: Expert, long

Summary

Amazon Web Services (AWS) Agentic AI organization's Stefano Soatto argues that intelligence in AI models is fundamentally about time, not merely parameter count. The research posits that as AI models scale, they risk entering a "savant regime" where performance gains on benchmarks come with decreased insight, solving problems through brute force rather than reasoning. The proposed solution involves training models to operate transductively by minimizing inference time, which encourages learning the algorithmic structure of data rather than just statistical patterns. This approach contrasts with classical statistical learning theory, which prioritizes regularization to avoid overfitting. The article introduces a new paper demonstrating that reducing inference time maximizes the algorithmic mutual information between training data and future tasks, quantified by the relationship `log speed-up = I(h : D)`. This framework suggests designing models to predict the marginal value of computational costs and incorporating complexity costs into training targets.

Key takeaway

For research scientists developing large language models, you should shift focus from merely scaling parameter counts to optimizing for inference time. Incorporate complexity costs into your training objectives and design models to predict the marginal value of additional computation. This approach will encourage transductive reasoning and improve absolute performance on novel tasks, moving beyond the "savant regime" of brute-force problem-solving.

Key insights

Minimizing inference time in AI models fosters reasoning and algorithmic learning, counteracting the "savant regime" of brute-force scaling.

Principles

• Intelligence is about time, not parameter count.
• Transduction prioritizes reasoning over generalization.
• Algorithmic structure is learned by reducing inference time.

Method

Train models to predict the marginal value of inference costs and include complexity costs in training targets. This forces models to minimize time during inference, maximizing algorithmic mutual information.

In practice

• Design models to predict marginal inference costs.
• Include complexity costs in training targets.
• Utilize AI Functions for two-level control strategies.

Topics

• Transductive Inference
• Large Language Models
• Algorithmic Information Theory
• Inference Time Optimization
• AI Agent Design

Code references

• strands-labs/ai-functions

Best for: Research Scientist, AI Researcher, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Amazon Science homepage.